{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "range(3, 5)\n"
     ]
    }
   ],
   "source": [
    "a = 3\n",
    "\n",
    "if a in range(3,5):\n",
    "    print(a)\n",
    "    print(range(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = [0,1,2,3,4,5,6,7,8,9,10,'all']\n",
    "\n",
    "yp = [90.23,90.69,89.72,89.23,88.37,88.41,87.07,86.04,86.61,86.05,85.57,81.22] \n",
    "\n",
    "y =  [90.31,90.19,89.12,88.97,88.85,87.52,87.17,86.12,85.99,86.05,86.04,83.05]\n",
    "\n",
    "\n",
    "plt.plot(x,y,'b^',label = 'not freeze pooler')\n",
    "#plt.plot(x,y,'b')\n",
    "plt.plot(x,yp,'rs',label='freeze pooler')\n",
    "#plt.plot(x,yp,'r')\n",
    "\n",
    "\n",
    "plt.title('Layer Freeze Performance')\n",
    "plt.xlabel('Layer')\n",
    "plt.ylabel('F1 score')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
